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Facial recognition technology and deep learning models: Empowering the modern-day industries

Facial recognition technology and deep learning models: Empowering the modern-day industries

By Damien Martin, Marketing Executive at Shufti Pro

The technological revolution has fueled the growth of all industries worldwide but it has also enabled bad actors to bypass the security protocols of corporations. With the combination of Deep Learning (DL) and facial verification systems, industries can leverage big datasets to learn useful representations of faces in real time. According to statistics, the market size of the online face verification industry was around $5 billion in the year 2021, and it is forecasted to reach approximately $13 billion by 2028. Facial recognition technology via deep learning models will be a foolproof approach for deterring fraud in the corporate world.

Facial recognition technology: The impact of deep learning algorithms

The blend of deep learning models with facial recognition technology has enhanced the service in terms of speed and accuracy. DL, which is derived from machine learning, is integral to the rise of artificial intelligence technology worldwide. It holds the power to improve the accuracy of facial recognition systems by using trained Convolutional Neural Networks (CNNs).

​​Historically, face recognition via eigenfaces was the focus of research. It was an incredible milestone because the project achieved exceptional results and highlighted the importance of holistic approaches. In this technique, facial data is projected onto a feature area, also known as face space. The purpose is to encode the variation in pre-existing face images. The space is explicitly explained by the “eigenfaces,” which are the eigenvectors of the set of faces.

A survey provided meaningful insights into the role of face recognition research, emphasizing the trend from holistic learning methods (e.g., eigenfaces) to the recent deep learning approaches. Implementing deep learning models enhanced task performance to the highest accuracy in just three years of its use.

Top 4 turning points for deep learning algorithms

Four major milestones that have contributed to the transformation of face recognition technology via DL models are the following:

  • DeepFace
  • DeepID series of systems
  • VGGFace
  • FaceNet

DeepFace is a system based on Convolutional Neural Networks (CNNs). It was a significant step towards using deep learning models for face recognition, accomplishing human-like performance on all given tasks. The method achieved the highest precision on the Labeled Faces in the Wild (LFW) dataset, reducing the errors considerably.

The DeepID or Deep hidden IDentity can learn high-level facial feature representation for face authentication. Initially, a major challenge of face recognition technology was to show clear face feature representations. Such types of systems were among the first DL models to accomplish human-like performance. For instance, DeepID2 accomplished the highest accuracy rates on the Labeled Faces in the Wild (LFW) dataset.

FaceNet in 2015 achieved top-notch results with the help of the triplet loss function. This enabled experts to encode images effectively as feature vectors. In this way, professionals could instantly evaluate similarity calculation and matching with the help of distance computations. The system directly learned mapping from facial data to a compact Euclidean space, where the approach used a CNN model to achieve desired results.

Experts trained the system to directly optimize the embedding rather than forming an intermediate bottleneck layer. They implemented triplets of approximate matching/ non-matching face patches for training, which were produced via a triplet mining technique.

Omkar Parkhi introduced the system of VGGFace, containing 3.31 million images from 9131 subjects. The focus of his team’s work was on collecting an extensive training dataset and using it to train Deep Convolutional Neural Networks (DCNNs) for powerful face recognition. Consequently, they showed how a big dataset (2.6M images, over 2.6K people) can be assembled with the help of automation and an expert in the loop.

Various use cases of facial recognition technology in modern-day industries

  • In the healthcare industry, experts can easily integrate biometric services with the pre-existing security system to verify patients’ identities. It can automate manual verification procedures and overcome fraud cases in hospitals by monitoring check-ins and outs of patients.
  • In retail, clients can install the software in stores to offer contact-free payment methods. It can also ensure a positive experience for consumers as they do not have to wait in long queues.
  • Facial Biometric Technology (FBT) can help enhance on-premises security and maintain student attendance in the education sector. Incorporating FBT in educational institutions can help security departments deter fraud, giving pupils protection from external threats.
  • In the financial industry, the implementation of biometrics can verify clients’ identities, improves payment transactions, and ensures compliance with KYC/AML regulations.

The final verdict

In a nutshell, implementing facial recognition technology is a pragmatic approach for businesses nowadays. Industries around the globe have been applying facial verification technology for enhancing security and strengthening relationships with their clients. Moreover, corporations can easily abide by the latest AML/KYC regulations and avoid hefty fines. With the combination of face verification and deep learning models, enterprises can streamline their customer onboarding and achieve their business milestones timely.

About the author

Damien Martin is a Marketing Executive at Shufti Pro, an AI-powered identity verification service provider.

DISCLAIMER: Biometric Update’s Industry Insights are submitted content. The views expressed in this post are that of the author, and don’t necessarily reflect the views of Biometric Update.

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